SpringerLink
Log in

Current Imaging Modalities and Virtual Models for Kidney Tumors

  • Chapter
  • First Online:
Robotic Urologic Surgery

  • 1017 Accesses

Abstract

During the last decades, the progressive diffusion and technological improvements of renal tumor imaging have played a pivotal role in changing the natural history of this disease.

The widespread diffusion of ultrasound (US) and computed tomography (CT) scan increased the number of incidentally detected renal tumors and amenable to less invasive or conservative treatment has increased with a subsequent increase in overall and cancer-specific survival.

Moreover, the more frequent application of contrast-enhanced ultrasound (CEUS) and magnetic resonance imaging (MRI) lets clinicians to progressively better characterize some type of masses and help the surgeon choose the better treatment option.

In addition, a lot of imaging technologies have been adopted for intraoperative use. The intraoperative US and the indocyanine green guidance are two of the most diffused tools adopted to improve the surgical navigation.

Finally, the recent advent of 3D virtual models has furtherly increased surgeons’ comprehension of anatomical details with a subsequently more accurate preoperative planning. These very promising technologies had a large variety of applications, from 3D printed models to mixed or augmented reality.

In conclusion the state of the art in pre- and intraoperative imaging modalities for kidney tumors is rapidly evolving thanks to technological improvements. With the help of these new technologies, it is estimated to further increase the number of complex renal masses suitable for nephron-sparing surgery and to reduce the postoperative functional impairment thanks to more conservative resection techniques and more selective clam** procedures.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 67.40
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 85.59
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 117.69
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ljungberg B, Albiges L, Abu-Ghanem Y, Bensalah K, Dabestani S, Fernández-Pello S, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: limited update March 2021. Eur Urol [Internet]. 2021;75(5):799–810. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0302283821000075

    Article  Google Scholar 

  2. van Oostenbrugge TJ, Fütterer JJ, Mulders PFA. Diagnostic imaging for solid renal tumors: a pictorial review. Kidney Cancer. 2018;2(2):79–93.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Rumack C, Wilson S. Diagnostic ultrasound: general adult. 4th ed. Saunders; 2014.

    Google Scholar 

  4. Bertolotto M, Bucci S, Valentino M, Currò F, Sachs C, Cova MA. Contrast-enhanced ultrasound for characterizing renal masses. Eur J Radiol [Internet]. 2018;105(May):41–8. Available from: https://doi.org/10.1016/j.ejrad.2018.05.015

  5. Bertolotto M, Cicero C, Catalano O, Currò F, Derchi LE. Solid renal tumors isoenhancing to kidneys on contrast-enhanced sonography: differentiation from pseudomasses. J Ultrasound Med. 2018;37(1):233–42.

    Article  PubMed  Google Scholar 

  6. Defortescu G, Cornu JN, Béjar S, Giwerc A, Gobet F, Werquin C, et al. Diagnostic performance of contrast-enhanced ultrasonography and magnetic resonance imaging for the assessment of complex renal cysts: a prospective study. Int J Urol. 2017;24(3):184–9.

    Article  PubMed  Google Scholar 

  7. Hoeffel C, Pousset M, Timsit MO, Elie C, Méjean A, Merran S, et al. Radiofrequency ablation of renal tumours: diagnostic accuracy of contrast-enhanced ultrasound for early detection of residual tumour. Eur Radiol. 2010;20(8):1812–21.

    Article  PubMed  Google Scholar 

  8. Hekman MCH, Rijpkema M, Langenhuijsen JF, Boerman OC, Oosterwijk E, Mulders PFA. Intraoperative imaging techniques to support complete tumor resection in partial nephrectomy. Eur Urol Focus [Internet]. 2018;4(6):960–8. Available from: https://doi.org/10.1016/j.euf.2017.04.008

  9. Correas JM, Anglicheau D, Joly D, Gennisson JL, Tanter M, Hélénon O. Ultrasound-based imaging methods of the kidney—recent developments. Kidney Int. 2016;90(6):1199–210.

    Article  PubMed  Google Scholar 

  10. Diaz de Leon A, Pedrosa I. Imaging and screening of kidney cancer. Radiol Clin North Am. 2017;55(6):1235–50.

    Article  PubMed  Google Scholar 

  11. Sasaguri K, Takahashi N. CT and MR imaging for solid renal mass characterization. Eur J Radiol [Internet]. 2018;99(Dec 2017):40–54. Available from: https://doi.org/10.1016/j.ejrad.2017.12.008

  12. Silverman SG, Pedrosa I, Ellis JH, Hindman NM, Wang ZJ, Chandarana H, et al. Bosniak classification of cystic renal masses, version 2019 : an update proposal and needs assessment. Radiology. 2019;292:26–38.

    Google Scholar 

  13. Wang ZJ, Westphalen AC, Zagoria RJ. CT and MRI of small renal masses. Br J Radiol. 2018;91(1087):20180131.

    Google Scholar 

  14. Johnson BA, Kim S, Steinberg RL, de Leon AD, Pedrosa I, Cadeddu JA. Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging. Urol Oncol Semin Orig Invest. 2019;37(12):941–6.

    Google Scholar 

  15. Steinberg RL, Rasmussen RG, Johnson BA, Ghandour R, De Leon AD, ** Y, et al. Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging. Eur Radiol. 2021;31(1):314–24.

    Article  PubMed  Google Scholar 

  16. Krane LS, Manny TB, Hemal AK. Is near infrared fluorescence imaging using indocyanine green dye useful in robotic partial nephrectomy: a prospective comparative study of 94 patients. Urology [Internet]. 2012;80(1):110–8. Available from: https://doi.org/10.1016/j.urology.2012.01.076

  17. Bjurlin MA, McClintock TR, Stifelman MD. Near-infrared fluorescence imaging with intraoperative administration of indocyanine green for robotic partial nephrectomy. Curr Urol Rep. 2015;16(4)

    Google Scholar 

  18. Diana P, Buffi NM, Lughezzani G, Dell’Oglio P, Mazzone E, Porter J, et al. The role of intraoperative indocyanine green in robot-assisted partial nephrectomy: results from a large, multi-institutional series. Eur Urol. 2020;78(5):743–9.

    Article  PubMed  Google Scholar 

  19. Veccia A, Antonelli A, Hampton LJ, Greco F, Perdonà S, Lima E, et al. Near-infrared fluorescence imaging with indocyanine green in robot-assisted partial nephrectomy: pooled analysis of comparative studies. Eur Urol Focus. 2020;6(3):505–12.

    Article  PubMed  Google Scholar 

  20. Autorino R, Porpiglia F, Dasgupta P, Rassweiler J, Catto JW, Hampton LJ, et al. Precision surgery and genitourinary cancers. Eur J Surg Oncol [Internet]. 2017;43(5):893–908. Available from: https://doi.org/10.1016/j.ejso.2017.02.005

    Article  CAS  PubMed  Google Scholar 

  21. Feußner H, Park A. Surgery 4.0: the natural culmination of the industrial revolution? Innov Surg Sci. 2017;2(3):105–8.

    PubMed  PubMed Central  Google Scholar 

  22. Fishman EK, Ney DR, Heath DG, Corl FM, Horton KM, Johnson PT. Volume rendering versus maximum intensity projection in CT angiography: what works best, when, and why. Radiographics. 2006;26(3):905–22.

    Article  PubMed  Google Scholar 

  23. Maddox MM, Feibus A, Liu J, Wang J, Thomas R, Silberstein JL. 3D-printed soft-tissue physical models of renal malignancies for individualized surgical simulation: a feasibility study. J Robot Surg. 2018;12(1):27–33.

    Article  PubMed  Google Scholar 

  24. Bernhard JC, Isotani S, Matsugasumi T, Duddalwar V, Hung AJ, Suer E, et al. Personalized 3D printed model of kidney and tumor anatomy: a useful tool for patient education. World J Urol. 2016;34(3):337–45.

    Article  PubMed  Google Scholar 

  25. Porpiglia F, Amparore D, Checcucci E, Autorino R, Manfredi M, Iannizzi G, et al. Current use of three-dimensional model technology in urology: a road map for personalised surgical planning. Eur Urol Focus [Internet]. 2018;4(5):652–6. Available from: https://doi.org/10.1016/j.euf.2018.09.012

  26. Porpiglia F, Fiori C, Checcucci E, Amparore D, Bertolo R. Hyperaccuracy three-dimensional reconstruction is able to maximize the efficacy of selective clam** during robot-assisted partial nephrectomy for complex renal masses. Eur Urol [Internet]. 2018;74(5):651–60. Available from: https://doi.org/10.1016/j.eururo.2017.12.027

  27. Shirk JD, Thiel DD, Wallen EM, Linehan JM, White WM, Badani KK, et al. Effect of 3-dimensional virtual reality models for surgical planning of robotic-assisted partial nephrectomy on surgical outcomes: a randomized clinical trial. JAMA Netw Open. 2019;2(9):1–11.

    Article  Google Scholar 

  28. Michiels C, Khene ZE, Prudhomme T, Boulenger de Hauteclocque A, Cornelis FH, Percot M, et al. 3D-Image guided robotic-assisted partial nephrectomy: a multi-institutional propensity score-matched analysis (UroCCR study 51). World J Urol [Internet]. 2021;0123456789. Available from: https://doi.org/10.1007/s00345-021-03645-1

  29. Antonelli A, Veccia A, Palumbo C, Peroni A, Mirabella G, Cozzoli A, et al. Holographic reconstructions for preoperative planning before partial nephrectomy: a head-to-head comparison with standard CT scan. Urol Int. 2019;102(2):212–7.

    Article  PubMed  Google Scholar 

  30. Checcucci E, Amparore D, Pecoraro A, Peretti D, Aimar R, De Cillis S, et al. 3D mixed reality holograms for preoperative surgical planning of nephron-sparing surgery: evaluation of surgeons’ perception. Minerva Urol Nefrol. 2021;73(3):367–75.

    Google Scholar 

  31. Su LM, Vagvolgyi BP, Agarwal R, Reiley CE, Taylor RH, Hager GD. Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration. Urology [Internet]. 2009;73(4):896–900. Available from: https://doi.org/10.1016/j.urology.2008.11.040

  32. Wake N, Bjurlin MA, Rostami P, Chandarana H, Huang WC. Three-dimensional printing and augmented reality: enhanced precision for robotic assisted partial nephrectomy. Urology [Internet]. 2018;116:227–8. Available from: https://doi.org/10.1016/j.urology.2017.12.038

  33. Porpiglia F, Checcucci E, Amparore D, Piramide F, Volpi G, Granato S, et al. Three-dimensional augmented reality robot-assisted partial nephrectomy in case of complex tumours (PADUA ≥10): a new intraoperative tool overcoming the ultrasound guidance. Eur Urol [Internet]. 2020;78(2):229–38. Available from: https://doi.org/10.1016/j.eururo.2019.11.024

  34. Nosrati MS, Abugharbieh R, Peyrat JM, Abinahed J, Al-Alao O, Al-Ansari A, et al. Simultaneous multi-structure segmentation and 3D nonrigid pose estimation in image-guided robotic surgery. IEEE Trans Med Imaging. 2016;35(1):1–12.

    Article  PubMed  Google Scholar 

  35. Amparore D, Checcucci E, Piazzolla P, Piramide F, De Cillis S, Piana A, et al. Indocyanine green drives computer vision based 3D augmented reality robot assisted partial nephrectomy: the beginning of “automatic” overlap** era. Urology. 2022;19:S0090–4295(22)00029–2. https://doi.org/10.1016/j.urology.2021.10.053. Epub ahead of print. PMID: 35063460.

Download references

Author information

Authors and Affiliations

  1. Department of Oncology, Division of Urology, University of Turin, Orbassano (Turin), Italy

    F. Porpiglia & F. Piramide

  2. Department of Urology, Vattikuti Urology Institute, Detroit, MI, USA

    C. Rogers

  3. ORSI Academy, Melle, Belgium

    P. De Backer

Authors
  1. F. Porpiglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Rogers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. De Backer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Piramide
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Urology, Icahn School of Medicine at Mount Sinai, New York, USA

    Peter Wiklund

  2. Department of Urology, ORSI Academy, Aalst, Belgium

    Alexandre Mottrie

  3. Pediatric Urology, University of Chicago Medicine and Biological Sciences, Chicago, IL, USA

    Mohan S Gundeti

  4. Urology, Global Robotics Institute, Celebration, FL, USA

    Vipul Patel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Porpiglia, F., Rogers, C., De Backer, P., Piramide, F. (2022). Current Imaging Modalities and Virtual Models for Kidney Tumors. In: Wiklund, P., Mottrie, A., Gundeti, M.S., Patel, V. (eds) Robotic Urologic Surgery. Springer, Cham. https://doi.org/10.1007/978-3-031-00363-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-00363-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00362-2

  • Online ISBN: 978-3-031-00363-9

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation